Fixed a few .csv and .xlsx file processing bugs

  parser.add_argument("--input-file", required=1,

                      help="Input file with data.")

  parser.add_argument("--input-type", default="csv",

                      choices=["csv", "pandas", "sdf"],

                      choices=["xlsx", "csv", "pandas", "sdf"],

                      help="Type of input file. If pandas, input must be a pkl.gz\n"

                           "containing a pandas dataframe. If sdf, should be in\n"

                           "(perhaps gzipped) sdf file.")

  # right option here might be to create a class which internally handles data

  # loading.

  if input_type == "xlsx":

    W = px.load_workbook(xlsx_file, use_iterators=True)

    p = W.get_sheet_by_name(name="Sheet1")

    W = px.load_workbook(input_file, use_iterators=True)

    sheet_names = W.get_sheet_names()

    p = W.get_sheet_by_name(name=sheet_names[0])    # Take first sheet as the active sheet

    return p.iter_rows()

  elif input_type == "csv":

    with open(csv_file, "rb") as f:

      reader = csv.reader(f, delimiter="\t")

    with open(input_file, "rb") as f:

      reader = csv.reader(f, delimiter=",")

      return [row for row in reader]

  elif input_type == "pandas":

    with gzip.open(input_file) as f:

    print row_index

    print raw_row

    # Skip row labels.

    if row_index == 0 or raw_row is None:

      continue

    #if row_index == 0 or raw_row is None:  #but if data has no rows, then this will skip 1st compound 

      #continue

    row, row_data = {}, get_row_data(raw_row, input_type, fields, field_types)

    for ind, (field, field_type) in enumerate(zip(fields, field_types)):

      row[field] = process_field(row_data[ind], field_type)